Recommendations based on Capability in E-Learning Systems using ABC with PSO to Improve Learning Performance

Abstract

Web-based learning is often called online learning or e-learning because it includes online course content. Searching for patterns of Web access, web access patterns, web structures, and web content dynamics is known as Web Mining. The recommender system given for personalized recommendations is on a set of objects and their utility to a certain domain which begins from the available information on objects and users. One of the most popular analysis of data is clustering, which divides data into relevant clusters. In this work, a Memetic Swarm Clustering (MSC) is proposed, which is based on Artificial Bee Colony (ABC) along with the Particle Swarm Optimization (PSO) and K-Means algorithm. It makes use of the algorithm K-means clustering for categorizing the users on the basis of their interests. The results of the experiment proved that the proposed MSC method improved performance.